.. _l-onnx-docai.onnx.ml-TreeEnsembleClassifier: =================================== ai.onnx.ml - TreeEnsembleClassifier =================================== .. contents:: :local: .. _l-onnx-opai-onnx-ml-treeensembleclassifier-3: TreeEnsembleClassifier - 3 (ai.onnx.ml) ======================================= **Version** * **name**: `TreeEnsembleClassifier (GitHub) `_ * **domain**: **ai.onnx.ml** * **since_version**: **3** * **function**: False * **support_level**: SupportType.COMMON * **shape inference**: True This version of the operator has been available **since version 3 of domain ai.onnx.ml**. **Summary** Tree Ensemble classifier. Returns the top class for each of N inputs. The attributes named 'nodes_X' form a sequence of tuples, associated by index into the sequences, which must all be of equal length. These tuples define the nodes. Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves. A leaf may have multiple votes, where each vote is weighted by the associated class_weights index. One and only one of classlabels_strings or classlabels_int64s will be defined. The class_ids are indices into this list. All fields ending with _as_tensor can be used instead of the same parameter without the suffix if the element type is double and not float. **Attributes** * **base_values**: Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0) * **base_values_as_tensor**: Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0) * **class_ids**: The index of the class list that each weight is for. * **class_nodeids**: node id that this weight is for. * **class_treeids**: The id of the tree that this node is in. * **class_weights**: The weight for the class in class_id. * **class_weights_as_tensor**: The weight for the class in class_id. * **classlabels_int64s**: Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined. * **classlabels_strings**: Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined. * **nodes_falsenodeids**: Child node if expression is false. * **nodes_featureids**: Feature id for each node. * **nodes_hitrates**: Popularity of each node, used for performance and may be omitted. * **nodes_hitrates_as_tensor**: Popularity of each node, used for performance and may be omitted. * **nodes_missing_value_tracks_true**: For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes. * **nodes_modes**: The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF' * **nodes_nodeids**: Node id for each node. Ids may restart at zero for each tree, but it not required to. * **nodes_treeids**: Tree id for each node. * **nodes_truenodeids**: Child node if expression is true. * **nodes_values**: Thresholds to do the splitting on for each node. * **nodes_values_as_tensor**: Thresholds to do the splitting on for each node. * **post_transform**: Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' Default value is ``'NONE'``. **Inputs** * **X** (heterogeneous) - **T1**: Input of shape [N,F] **Outputs** * **Y** (heterogeneous) - **T2**: N, Top class for each point * **Z** (heterogeneous) - **tensor(float)**: The class score for each class, for each point, a tensor of shape [N,E]. **Type Constraints** * **T1** in ( tensor(double), tensor(float), tensor(int32), tensor(int64) ): The input type must be a tensor of a numeric type. * **T2** in ( tensor(int64), tensor(string) ): The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used. **Examples** **Differences** .. raw:: html

`0`	`0`	`Tree Ensemble classifier. Returns the top class for each of N inputs.`	`Tree Ensemble classifier. Returns the top class for each of N inputs.`
`1`	`1`
`2`	`2`	`The attributes named 'nodes_X' form a sequence of tuples, associated by`	`The attributes named 'nodes_X' form a sequence of tuples, associated by`
`3`	`3`	`index into the sequences, which must all be of equal length. These tuples`	`index into the sequences, which must all be of equal length. These tuples`
`4`	`4`	`define the nodes.`	`define the nodes.`
`5`	`5`
`6`	`6`	`Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves.`	`Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves.`
`7`	`7`	`A leaf may have multiple votes, where each vote is weighted by`	`A leaf may have multiple votes, where each vote is weighted by`
`8`	`8`	`the associated class_weights index.`	`the associated class_weights index.`
`9`	`9`
`10`	`10`	`One and only one of classlabels_strings or classlabels_int64s`	`One and only one of classlabels_strings or classlabels_int64s`
`11`	`11`	`will be defined. The class_ids are indices into this list.`	`will be defined. The class_ids are indices into this list.`
	`12`		`All fields ending with _as_tensor can be used instead of the`
	`13`		`same parameter without the suffix if the element type is double and not float.`
`12`	`14`
`13`	`15`	`Attributes`	`Attributes`
`14`	`16`
`15`	`17`	`* base_values:`	`* base_values:`
`16`	`18`	`Base values for classification, added to final class score; the size`	`Base values for classification, added to final class score; the size`
`17`	`19`	`must be the same as the classes or can be left unassigned (assumed`	`must be the same as the classes or can be left unassigned (assumed`
`18`	`20`	`0)`	`0)`
	`21`		`* base_values_as_tensor:`
	`22`		`Base values for classification, added to final class score; the size`
	`23`		`must be the same as the classes or can be left unassigned (assumed`
	`24`		`0)`
`19`	`25`	`* class_ids:`	`* class_ids:`
`20`	`26`	`The index of the class list that each weight is for.`	`The index of the class list that each weight is for.`
`21`	`27`	`* class_nodeids:`	`* class_nodeids:`
`22`	`28`	`node id that this weight is for.`	`node id that this weight is for.`
`23`	`29`	`* class_treeids:`	`* class_treeids:`
`24`	`30`	`The id of the tree that this node is in.`	`The id of the tree that this node is in.`
`25`	`31`	`* class_weights:`	`* class_weights:`
`26`	`32`	`The weight for the class in class_id.`	`The weight for the class in class_id.`
	`33`		`* class_weights_as_tensor:`
	`34`		`The weight for the class in class_id.`
`27`	`35`	`* classlabels_int64s:`	`* classlabels_int64s:`
`28`	`36`	`Class labels if using integer labels. One and only one of the`	`Class labels if using integer labels. One and only one of the`
`29`	`37`	`'classlabels_*' attributes must be defined.`	`'classlabels_*' attributes must be defined.`
`30`	`38`	`* classlabels_strings:`	`* classlabels_strings:`
`31`	`39`	`Class labels if using string labels. One and only one of the`	`Class labels if using string labels. One and only one of the`
`32`	`40`	`'classlabels_*' attributes must be defined.`	`'classlabels_*' attributes must be defined.`
`33`	`41`	`* nodes_falsenodeids:`	`* nodes_falsenodeids:`
`34`	`42`	`Child node if expression is false.`	`Child node if expression is false.`
`35`	`43`	`* nodes_featureids:`	`* nodes_featureids:`
`36`	`44`	`Feature id for each node.`	`Feature id for each node.`
`37`	`45`	`* nodes_hitrates:`	`* nodes_hitrates:`
`38`	`46`	`Popularity of each node, used for performance and may be omitted.`	`Popularity of each node, used for performance and may be omitted.`
	`47`		`* nodes_hitrates_as_tensor:`
	`48`		`Popularity of each node, used for performance and may be omitted.`
`39`	`49`	`* nodes_missing_value_tracks_true:`	`* nodes_missing_value_tracks_true:`
`40`	`50`	`For each node, define what to do in the presence of a missing value:`	`For each node, define what to do in the presence of a missing value:`
`41`	`51`	`if a value is missing (NaN), use the 'true' or 'false' branch based`	`if a value is missing (NaN), use the 'true' or 'false' branch based`
`42`	`52`	`on the value in this array. This attribute may be left undefined,`	`on the value in this array. This attribute may be left undefined,`
`43`	`53`	`and the defalt value is false (0) for all nodes.`	`and the defalt value is false (0) for all nodes.`
`44`	`54`	`* nodes_modes:`	`* nodes_modes:`
`45`	`55`	`The node kind, that is, the comparison to make at the node. There is`	`The node kind, that is, the comparison to make at the node. There is`
`46`	`56`	`no comparison to make at a leaf node. One of 'BRANCH_LEQ',`	`no comparison to make at a leaf node. One of 'BRANCH_LEQ',`
`47`	`57`	`'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ',`	`'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ',`
`48`	`58`	`'LEAF'`	`'LEAF'`
`49`	`59`	`* nodes_nodeids:`	`* nodes_nodeids:`
`50`	`60`	`Node id for each node. Ids may restart at zero for each tree, but it`	`Node id for each node. Ids may restart at zero for each tree, but it`
`51`	`61`	`not required to.`	`not required to.`
`52`	`62`	`* nodes_treeids:`	`* nodes_treeids:`
`53`	`63`	`Tree id for each node.`	`Tree id for each node.`
`54`	`64`	`* nodes_truenodeids:`	`* nodes_truenodeids:`
`55`	`65`	`Child node if expression is true.`	`Child node if expression is true.`
`56`	`66`	`* nodes_values:`	`* nodes_values:`
`57`	`67`	`Thresholds to do the splitting on for each node.`	`Thresholds to do the splitting on for each node.`
	`68`		`* nodes_values_as_tensor:`
	`69`		`Thresholds to do the splitting on for each node.`
`58`	`70`	`* post_transform:`	`* post_transform:`
`59`	`71`	`Indicates the transform to apply to the score. One of 'NONE,'`	`Indicates the transform to apply to the score. One of 'NONE,'`
`60`	`72`	`'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' Default value is 'NONE'.`	`'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' Default value is 'NONE'.`
`61`	`73`
`62`	`74`	`Inputs`	`Inputs`
`63`	`75`
`64`	`76`	`* X (heterogeneous) - T1:`	`* X (heterogeneous) - T1:`
`65`	`77`	`Input of shape [N,F]`	`Input of shape [N,F]`
`66`	`78`
`67`	`79`	`Outputs`	`Outputs`
`68`	`80`
`69`	`81`	`* Y (heterogeneous) - T2:`	`* Y (heterogeneous) - T2:`
`70`	`82`	`N, Top class for each point`	`N, Top class for each point`
`71`	`83`	`* Z (heterogeneous) - tensor(float):`	`* Z (heterogeneous) - tensor(float):`
`72`	`84`	`The class score for each class, for each point, a tensor of shape`	`The class score for each class, for each point, a tensor of shape`
`73`	`85`	`[N,E].`	`[N,E].`
`74`	`86`
`75`	`87`	`Type Constraints`	`Type Constraints`
`76`	`88`
`77`	`89`	`* T1 in (`	`* T1 in (`
`78`	`90`	`tensor(double),`	`tensor(double),`
`79`	`91`	`tensor(float),`	`tensor(float),`
`80`	`92`	`tensor(int32),`	`tensor(int32),`
`81`	`93`	`tensor(int64)`	`tensor(int64)`
`82`	`94`	`):`	`):`
`83`	`95`	`The input type must be a tensor of a numeric type.`	`The input type must be a tensor of a numeric type.`
`84`	`96`	`* T2 in (`	`* T2 in (`
`85`	`97`	`tensor(int64),`	`tensor(int64),`
`86`	`98`	`tensor(string)`	`tensor(string)`
`87`	`99`	`):`	`):`
`88`	`100`	`The output type will be a tensor of strings or integers, depending`	`The output type will be a tensor of strings or integers, depending`
`89`	`101`	`on which of the classlabels_* attributes is used.`	`on which of the classlabels_* attributes is used.`

.. _l-onnx-opai-onnx-ml-treeensembleclassifier-1: TreeEnsembleClassifier - 1 (ai.onnx.ml) ======================================= **Version** * **name**: `TreeEnsembleClassifier (GitHub) `_ * **domain**: **ai.onnx.ml** * **since_version**: **1** * **function**: False * **support_level**: SupportType.COMMON * **shape inference**: True This version of the operator has been available **since version 1 of domain ai.onnx.ml**. **Summary** Tree Ensemble classifier. Returns the top class for each of N inputs. The attributes named 'nodes_X' form a sequence of tuples, associated by index into the sequences, which must all be of equal length. These tuples define the nodes. Similarly, all fields prefixed with 'class_' are tuples of votes at the leaves. A leaf may have multiple votes, where each vote is weighted by the associated class_weights index. One and only one of classlabels_strings or classlabels_int64s will be defined. The class_ids are indices into this list. **Attributes** * **base_values**: Base values for classification, added to final class score; the size must be the same as the classes or can be left unassigned (assumed 0) * **class_ids**: The index of the class list that each weight is for. * **class_nodeids**: node id that this weight is for. * **class_treeids**: The id of the tree that this node is in. * **class_weights**: The weight for the class in class_id. * **classlabels_int64s**: Class labels if using integer labels.
One and only one of the 'classlabels_*' attributes must be defined. * **classlabels_strings**: Class labels if using string labels.
One and only one of the 'classlabels_*' attributes must be defined. * **nodes_falsenodeids**: Child node if expression is false. * **nodes_featureids**: Feature id for each node. * **nodes_hitrates**: Popularity of each node, used for performance and may be omitted. * **nodes_missing_value_tracks_true**: For each node, define what to do in the presence of a missing value: if a value is missing (NaN), use the 'true' or 'false' branch based on the value in this array.
This attribute may be left undefined, and the defalt value is false (0) for all nodes. * **nodes_modes**: The node kind, that is, the comparison to make at the node. There is no comparison to make at a leaf node.
One of 'BRANCH_LEQ', 'BRANCH_LT', 'BRANCH_GTE', 'BRANCH_GT', 'BRANCH_EQ', 'BRANCH_NEQ', 'LEAF' * **nodes_nodeids**: Node id for each node. Ids may restart at zero for each tree, but it not required to. * **nodes_treeids**: Tree id for each node. * **nodes_truenodeids**: Child node if expression is true. * **nodes_values**: Thresholds to do the splitting on for each node. * **post_transform**: Indicates the transform to apply to the score.
One of 'NONE,' 'SOFTMAX,' 'LOGISTIC,' 'SOFTMAX_ZERO,' or 'PROBIT.' Default value is ``'NONE'``. **Inputs** * **X** (heterogeneous) - **T1**: Input of shape [N,F] **Outputs** * **Y** (heterogeneous) - **T2**: N, Top class for each point * **Z** (heterogeneous) - **tensor(float)**: The class score for each class, for each point, a tensor of shape [N,E]. **Type Constraints** * **T1** in ( tensor(double), tensor(float), tensor(int32), tensor(int64) ): The input type must be a tensor of a numeric type. * **T2** in ( tensor(int64), tensor(string) ): The output type will be a tensor of strings or integers, depending on which of the classlabels_* attributes is used.